Jetson Video Pipeline

Jetson Video Pipeline

Purpose

Build and run direct NVIDIA sample commands, then prove each consumer used the exact bytes produced by the preceding stage. This skill owns codec workflow and evidence policy; it does not own environment installation or product-support claims.

Terminal gates

Apply these before inspecting the target, another skill, or a command:

1. For a PSNR/SSIM-only request, state that objective quality measurement is outside this skill and requires a separately authorized workflow, return not_evaluated with reason out_of_scope, then stop. Do not name a tool or request media. Do the same for a request limited to capture, transport, AI, display, or glass-to-glass latency. For a mixed codec-plus-quality request, continue only the codec portion and report the quality portion as not_evaluated with reason out_of_scope rather than silently omitting it.

2. Classify every other request before applying the media gate:

   • Return planned for architecture and route questions; they need no media. State assumptions and what execution would verify, then stop before target inspection, retrieval, authentication, recipe dispatch, or workspace creation.
   • A dry run with concrete commands needs a user-named path or placeholder and the metadata required by those commands. The media need not exist. Obtain recipe options through jetson-video-recipe, report planned, and do not inspect, authenticate, or launch anything.
   • Execution, verification, and performance require one exact target-local media path or one user-supplied HTTP(S) URL. Without it, return input_required, identify the intended route briefly using only stages expressible by this skill's allowlisted samples, ask for that one item, and stop before target inspection, retrieval, authentication, recipe dispatch, or workspace creation. Label every other requested transform as unresolved rather than inventing an executable route.

   Never choose catalog or synthetic media. The deterministic setup smoke fixture is allowed only for a bounded capability operation and is never representative pipeline or performance evidence.

Select and authenticate the execution surface

Steps 3–4 apply to execution; step 5 also applies to recipe-bearing dry runs. For other planning, preserve an explicit surface without claiming live eligibility; delegated surface-specific dry runs return selection_required without ranking the choices.

3. Preserve explicit native, pynvc, and both. Map “whichever”, “best available”, “choose for me”, or otherwise delegated selection to auto, not both. For auto: zero eligible surfaces is blocked, one runs, and two is selection_required; do not rank them or consult old results.

4. Obtain a fresh read-only readiness result from jetson-video-setup through public skill dispatch. For Python, pass any exact user-supplied or current-conversation interpreter. Otherwise select the profile before dispatch: decode-performance may use pynvc-smoke; encode, segmentation, advanced/decode.py, pipeline, and encode-benchmark work require full-samples. Setup checks that profile's conventional path; never scan for a venv. If only the smoke profile is ready for full-samples work, return dependency_required before workspace creation and direct the user to provision a separate full-samples venv; never upgrade the smoke venv in place. Native eligibility requires one installed, package-verified SDK and one package-owned Samples root. Python eligibility requires that exact interpreter, an importable PyNvVideoCodec distribution loaded from its environment, and a clean pip check. The result must match the requested Jetson and GPU. Preserve the setup reason when a candidate is ineligible. The requested pipeline supplies its own operation proof.

5. Recipe-bearing encode and transcode work requires jetson-video-recipe; an acceptance request containing a performance stage also requires jetson-video-benchmark. Invoke either through public skill dispatch and pass its result as data. If a needed sibling is absent, preserve completed stages and say: I can run <stage>, but it requires <skill>, which is not installed. Install <skill> and retry this stage.

   “Preserve” means retain each completed stage's status and current path/size/SHA-256 identities in the user-facing partial result; it does not create hidden resumable state. On retry in the same request, reopen and rehash those artifacts and skip only unchanged complete stages. With a changed/missing identity, or a later request that does not supply the prior evidence, use a new workspace and rerun the stage.